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Fonts That Fit the Music: A Multimodal Design Trend Analysis of Lyric Videos

  • Published:2022 Issue: Volume:10 Page:65414-65425
  • ISSN:2169-3536
  • Container-title:IEEE Access
  • language:
  • Short-container-title:IEEE Access

Author:

Haraguchi Daichi1ORCID,Sakaguchi Shota2,Kato Jun3,Goto Masataka3ORCID,Uchida Seiichi4ORCID

Affiliation:

1. Graduate School of Advanced Information Technology, Kyushu University, Fukuoka, Japan

2. Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan

3. National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Tsukuba, Japan

4. Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan

Funder

Japan Society for the Promotion Science (JSPS) KAKENHI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference52 articles.

1. Multivariate autoregressive mixture models for music auto-tagging;coviello;Proc 13th Int Soc Music Inf Retr Conf (ISMIR),2012

2. Three current issues in music autotagging;marques;Proc 12th Int Soc Music Inf Retr Conf (ISMIR),2011

3. A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution

4. Serif or Sans: Visual Font Analytics on Book Covers and Online Advertisements

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lyricist-Singer Entropy Affects Lyric-Lyricist Classification Performance;2023 10th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA);2023-10-07
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